Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.
ObjectiveTo explore the actual effect of “graphic-sequenced memory method” in teaching electrocardiogram (ECG). MethodsOne hundred students were randomly divided into a traditional teaching group (n=50) and an innovative teaching group (n=50) in May, 2014. Teachers in the traditional teaching group utilized the traditional teaching outline, and teachers in the innovative teaching group received training in the new teaching method and syllabus. All students took an examination in the final semester by analyzing 20 ECGs from real clinical cases and gave their ECG reports. ResultsThe average ECG reading time was (32.0±4.8) minutes for the traditional teaching group and (18.0±3.6) minutes for the innovative teaching group. The average ECG accuracy results were (43.0±5.2)% for the traditional teaching group and (77.0±9.6)% for the innovative teaching group. ConclusionsECG learning is an important branch of the cardiac discipline, but ECG’s mechanisms are intricate and the learning content scattered. Textbooks tend to make students feel confused due to the restrictions of the length and format of the syllabi, and there are many other limitations. Graphic-sequenced memory method is a useful method which can be fully used in ECG teaching.
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.
Objective To conduct a systematic review on the Electrocardiogram (ECG) changes in the workers exposed to high temperatures by means of meta-analysis.Methods The retrospective cohort studies on the relationship between high temperature and ECG abnormalities published from 1990 to May 2009 were searched in CNKI, VIP, WanFang database and CBM database. The literatures meeting the inclusive criteria were selected, the quality was assessed, the data were extracted, and the meta-analyses were conducted with RevMan 4.2.2 software. Results A total of 20 studies were included. The results of meta-analyses showed: the ECG abnormality rate of the high-temperature group was obviously superior to that of the control group with significant difference (OR=2.76, 95%CI 2.37 to 3.20, Plt;0.000 01). The high-temperature severely affected left ventricular hypertrophy (OR=3.49, 95%CI 2.83 to 4.31, Plt;0.000 01), sinus bradycardia (OR=2.83, 95%CI 2.33 to 3.43, Plt;0.000 01), and changes in ST-T segment (OR=2.63, 95%CI 1.48 to 4.68, P=0.000 10), which indicated that the abnormal changes of ECG, such as left ventricular hypertrophy, sinus tachycardia, sinus bradycardia, and changes in ST-T segment could be the sensitive indexes to monitor cardiovascular disease of workers exposed to high-temperature. Conclusion The incidence of ECG abnormalities caused by high-temperature operation is obviously superior to that of the control group, so it is required to strengthen the health monitoring and labor protection for the workers exposed to high temperature.
Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
Objective To analyze the electrocardiogram (ECG) and troponin (cTnI) in patients with acute coronary syndrome (ACS), so as to assess their value in diagnosing the extent of vascular lesions. Methods The results of ECG, cTnI and coronary angiography (CAG) were analyzed in 37 patients with ACS. Chi-square test and a logistic regression model were used for statistical analysis. Results In patients with positive ECG or cTnI, the results of Chi-square test showed that the incidences of coronary occlusion (P=0.016, 0.003, respectively) and coronary stenosis (P=0.121, 0.013, respectively) were significantly higher than for those with negative ECG or cTnI. The results of logistic regression analysis indicated that only cTnI was significantly correlated with coronary occlusion (P=0.013) and moderate to severe coronary stenosis (P=0.021). ECG has significant consistency with cTnI (Kappa=0.617, Plt;0.001). Conclusion Both ECG and the qual itative cTnI test can reflect the extent of vascular lesions in patients with ACS.
ObjectiveTo find out the influencing factors of electrocardiogram (ECG) monitor configuration decision in surgical nursing units and form a scientific configuration standard, so as to provide a basis for the reasonable configuration of ECG monitors.MethodsFrom May to June 2018, the indexes and weights affecting the configuration of ECG monitors in surgical nursing units of a large public hospital were determined by interview survey method and analytic hierarchy process.ResultsThe influencing factors for configuration of ECG monitors in surgical nursing units were the number of operations, number of rescues, number of emergencies, number of deaths, and number of patients transferred to and out of intensive care unit, and the weights were 0.459 7, 0.224 9, 0.155 3, 0.111 2, and 0.049 0, respectively. The classification of nursing units was taken as plan, and the configuration standard of ECG monitors was established.ConclusionThe configuration model of ECG monitors in surgical nursing units based on analytic hierarchy process realizes the combination of qualitative and quantitative analysis, which provides scientific and reasonable reference for the configuration of ECG monitors.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional neural network (CNN) combined with one-dimensional gradient-weighted class activation mapping (1D Grad-CAM) for the automated detection of MI and the visualization of key waveform features in single-lead electrocardiograms (ECGs). The proposed method was evaluated using a total of 432 records from the Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) and the Normal Sinus Rhythm Database (NSRDB), comprising 334 MI and 98 normal ECGs. Experimental results demonstrated that the model achieved an accuracy, sensitivity, and specificity of 95.75%, 96.03%, and 95.47%, respectively, in MI detection. Furthermore, the visualization results indicated that the model’s decision-making process aligned closely with clinically critical features, including pathological Q waves, ST-segment elevation, and T-wave inversion. This study confirms that the proposed deep learning algorithm combined with explainable technology performs effectively in the intelligent diagnosis of MI and the visualization of critical ECG waveforms, demonstrating its potential as a useful tool for early MI risk assessment and computer-aided diagnosis.